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    "<!-- (evaluation)= -->\n",
    "# WeatherBench 2 Evaluation Quickstart\n",
    "\n",
    "<a target=\"_blank\" href=\"https://colab.research.google.com/github/google-research/weatherbench2/blob/main/docs/source/evaluation.ipynb\">\n",
    "  <img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/>\n",
    "</a>\n",
    "\n",
    "In this notebook, we will cover the basic functionality of the WeatherBench evaluation framework.\n",
    "\n",
    "The WeatherBench evaluation framework takes two datasets for forecast and ground truth (called obs, even though reanalysis datasets like ERA5 are not observations), computes and saves the specified metrics.\n",
    "\n",
    "Here, we will evalute ECMWF's HRES forecast against ERA5."
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   "id": "f361a7ba-fa4f-49a8-a3cc-5113f7eb3430",
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   "source": [
    "# Pip might complain about the Pandas version. The notebook should still work as expected.\n",
    "!pip install git+https://github.com/google-research/weatherbench2.git"
   ]
  },
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   "id": "70b4fd7a-d599-4d82-986f-4ec51743e98c",
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   "outputs": [],
   "source": [
    "import apache_beam   # Needs to be imported separately to avoid TypingError\n",
    "import weatherbench2\n",
    "import xarray as xr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f6b59ae2-4ca9-4b93-8e9f-6ac2304bad50",
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   "source": [
    "# Run the code below to access cloud data on Colab!\n",
    "# from google.colab import auth\n",
    "# auth.authenticate_user()"
   ]
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    "### Specify input datasets\n",
    "\n",
    "Let's take a look at the datasets. Currently, the WeatherBench pipeline requires all input dataset to be stored as Zarr files."
   ]
  },
  {
   "cell_type": "code",
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   "id": "4b6ec0cb-928f-4e50-a077-802b5a51e469",
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   "source": [
    "forecast_path = 'gs://weatherbench2/datasets/hres/2016-2022-0012-64x32_equiangular_conservative.zarr'\n",
    "obs_path = 'gs://weatherbench2/datasets/era5/1959-2022-6h-64x32_equiangular_conservative.zarr'\n",
    "climatology_path = 'gs://weatherbench2/datasets/era5-hourly-climatology/1990-2019_6h_64x32_equiangular_conservative.zarr'"
   ]
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    "Generally, we follow ECMWF's naming conventions for the input files.\n",
    "\n",
    "* `time` [np.datetime64]: Time at forecast is initialized\n",
    "* `lead_time` or `prediction_timedelta` [np.timedelta64]: Lead time\n",
    "* `latitude` [float]: Latitudes from -90 to 90\n",
    "* `longitude` [float]: Longitudes from 0 to 360\n",
    "* `level` [hPa]: Pressure levels (optional)\n",
    "\n",
    "We don't actually need to open the forecast and obs datasets at this point, but we will do so here to see their structure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "56de9923-acc3-49e8-90e9-a3286ff45d86",
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       "Dimensions:                   (time: 5114, prediction_timedelta: 41,\n",
       "                               longitude: 64, latitude: 32, level: 13)\n",
       "Coordinates:\n",
       "  * latitude                  (latitude) float64 -87.19 -81.56 ... 81.56 87.19\n",
       "  * level                     (level) int32 50 100 150 200 ... 700 850 925 1000\n",
       "  * longitude                 (longitude) float64 0.0 5.625 ... 348.8 354.4\n",
       "  * prediction_timedelta      (prediction_timedelta) timedelta64[ns] 00:00:00...\n",
       "  * time                      (time) datetime64[ns] 2016-01-01 ... 2022-12-31...\n",
       "Data variables: (12/16)\n",
       "    10m_u_component_of_wind   (time, prediction_timedelta, longitude, latitude) float32 dask.array&lt;chunksize=(4, 1, 64, 32), meta=np.ndarray&gt;\n",
       "    10m_v_component_of_wind   (time, prediction_timedelta, longitude, latitude) float32 dask.array&lt;chunksize=(4, 1, 64, 32), meta=np.ndarray&gt;\n",
       "    10m_wind_speed            (time, prediction_timedelta, longitude, latitude) float32 dask.array&lt;chunksize=(4, 1, 64, 32), meta=np.ndarray&gt;\n",
       "    2m_temperature            (time, prediction_timedelta, longitude, latitude) float32 dask.array&lt;chunksize=(4, 1, 64, 32), meta=np.ndarray&gt;\n",
       "    geopotential              (time, prediction_timedelta, level, longitude, latitude) float32 dask.array&lt;chunksize=(4, 1, 13, 64, 32), meta=np.ndarray&gt;\n",
       "    mean_sea_level_pressure   (time, prediction_timedelta, longitude, latitude) float32 dask.array&lt;chunksize=(4, 1, 64, 32), meta=np.ndarray&gt;\n",
       "    ...                        ...\n",
       "    total_precipitation_24hr  (time, prediction_timedelta, longitude, latitude) float32 dask.array&lt;chunksize=(4, 1, 64, 32), meta=np.ndarray&gt;\n",
       "    total_precipitation_6hr   (time, prediction_timedelta, longitude, latitude) float32 dask.array&lt;chunksize=(4, 1, 64, 32), meta=np.ndarray&gt;\n",
       "    u_component_of_wind       (time, prediction_timedelta, level, longitude, latitude) float32 dask.array&lt;chunksize=(4, 1, 13, 64, 32), meta=np.ndarray&gt;\n",
       "    v_component_of_wind       (time, prediction_timedelta, level, longitude, latitude) float32 dask.array&lt;chunksize=(4, 1, 13, 64, 32), meta=np.ndarray&gt;\n",
       "    vertical_velocity         (time, prediction_timedelta, level, longitude, latitude) float32 dask.array&lt;chunksize=(4, 1, 13, 64, 32), meta=np.ndarray&gt;\n",
       "    wind_speed                (time, prediction_timedelta, level, longitude, latitude) float32 dask.array&lt;chunksize=(4, 1, 13, 64, 32), meta=np.ndarray&gt;</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-0c761798-5fa2-482a-b5f2-5ac974a59285' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-0c761798-5fa2-482a-b5f2-5ac974a59285' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 5114</li><li><span class='xr-has-index'>prediction_timedelta</span>: 41</li><li><span class='xr-has-index'>longitude</span>: 64</li><li><span class='xr-has-index'>latitude</span>: 32</li><li><span class='xr-has-index'>level</span>: 13</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-1a46e36f-f087-472e-b4c7-2da574b25e54' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1a46e36f-f087-472e-b4c7-2da574b25e54' class='xr-section-summary' >Coordinates: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>latitude</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-87.19 -81.56 ... 81.56 87.19</div><input id='attrs-b96b4aa0-632d-4b88-aad8-b5676396707e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b96b4aa0-632d-4b88-aad8-b5676396707e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-918aa278-0d02-48d5-bd5a-9c5503f89e41' class='xr-var-data-in' type='checkbox'><label for='data-918aa278-0d02-48d5-bd5a-9c5503f89e41' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-87.1875, -81.5625, -75.9375, -70.3125, -64.6875, -59.0625, -53.4375,\n",
       "       -47.8125, -42.1875, -36.5625, -30.9375, -25.3125, -19.6875, -14.0625,\n",
       "        -8.4375,  -2.8125,   2.8125,   8.4375,  14.0625,  19.6875,  25.3125,\n",
       "        30.9375,  36.5625,  42.1875,  47.8125,  53.4375,  59.0625,  64.6875,\n",
       "        70.3125,  75.9375,  81.5625,  87.1875])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>level</span></div><div class='xr-var-dims'>(level)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>50 100 150 200 ... 700 850 925 1000</div><input id='attrs-d8e0dad2-6b56-433a-a290-e256170b06a9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d8e0dad2-6b56-433a-a290-e256170b06a9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dfcdee2d-841e-44b2-a343-42af2e0fba3b' class='xr-var-data-in' type='checkbox'><label for='data-dfcdee2d-841e-44b2-a343-42af2e0fba3b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  50,  100,  150,  200,  250,  300,  400,  500,  600,  700,  850,  925,\n",
       "       1000], dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>longitude</span></div><div class='xr-var-dims'>(longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 5.625 11.25 ... 348.8 354.4</div><input id='attrs-7b8ea71c-a859-43bf-bcac-6ac8d6df2687' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7b8ea71c-a859-43bf-bcac-6ac8d6df2687' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ecf42652-f06b-4d95-97c6-e634946f6d78' class='xr-var-data-in' type='checkbox'><label for='data-ecf42652-f06b-4d95-97c6-e634946f6d78' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0.   ,   5.625,  11.25 ,  16.875,  22.5  ,  28.125,  33.75 ,  39.375,\n",
       "        45.   ,  50.625,  56.25 ,  61.875,  67.5  ,  73.125,  78.75 ,  84.375,\n",
       "        90.   ,  95.625, 101.25 , 106.875, 112.5  , 118.125, 123.75 , 129.375,\n",
       "       135.   , 140.625, 146.25 , 151.875, 157.5  , 163.125, 168.75 , 174.375,\n",
       "       180.   , 185.625, 191.25 , 196.875, 202.5  , 208.125, 213.75 , 219.375,\n",
       "       225.   , 230.625, 236.25 , 241.875, 247.5  , 253.125, 258.75 , 264.375,\n",
       "       270.   , 275.625, 281.25 , 286.875, 292.5  , 298.125, 303.75 , 309.375,\n",
       "       315.   , 320.625, 326.25 , 331.875, 337.5  , 343.125, 348.75 , 354.375])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>prediction_timedelta</span></div><div class='xr-var-dims'>(prediction_timedelta)</div><div class='xr-var-dtype'>timedelta64[ns]</div><div class='xr-var-preview xr-preview'>0 days 00:00:00 ... 10 days 00:0...</div><input id='attrs-25aaa97a-77bc-4cca-84ad-4d9ff44f8b95' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-25aaa97a-77bc-4cca-84ad-4d9ff44f8b95' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f3e92038-67fa-4095-add9-5aabdd952204' class='xr-var-data-in' type='checkbox'><label for='data-f3e92038-67fa-4095-add9-5aabdd952204' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>time since forecast_reference_time</dd><dt><span>standard_name :</span></dt><dd>forecast_period</dd></dl></div><div class='xr-var-data'><pre>array([              0,  21600000000000,  43200000000000,  64800000000000,\n",
       "        86400000000000, 108000000000000, 129600000000000, 151200000000000,\n",
       "       172800000000000, 194400000000000, 216000000000000, 237600000000000,\n",
       "       259200000000000, 280800000000000, 302400000000000, 324000000000000,\n",
       "       345600000000000, 367200000000000, 388800000000000, 410400000000000,\n",
       "       432000000000000, 453600000000000, 475200000000000, 496800000000000,\n",
       "       518400000000000, 540000000000000, 561600000000000, 583200000000000,\n",
       "       604800000000000, 626400000000000, 648000000000000, 669600000000000,\n",
       "       691200000000000, 712800000000000, 734400000000000, 756000000000000,\n",
       "       777600000000000, 799200000000000, 820800000000000, 842400000000000,\n",
       "       864000000000000], dtype=&#x27;timedelta64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2016-01-01 ... 2022-12-31T12:00:00</div><input id='attrs-7111bdb1-c4a9-4a9a-8dac-79cc93732993' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7111bdb1-c4a9-4a9a-8dac-79cc93732993' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8b0d8120-d2e9-471a-9ca2-cbdcbadee377' class='xr-var-data-in' type='checkbox'><label for='data-8b0d8120-d2e9-471a-9ca2-cbdcbadee377' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>initial time of forecast</dd><dt><span>standard_name :</span></dt><dd>forecast_reference_time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2016-01-01T00:00:00.000000000&#x27;, &#x27;2016-01-01T12:00:00.000000000&#x27;,\n",
       "       &#x27;2016-01-02T00:00:00.000000000&#x27;, ..., &#x27;2022-12-30T12:00:00.000000000&#x27;,\n",
       "       &#x27;2022-12-31T00:00:00.000000000&#x27;, &#x27;2022-12-31T12:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-47ee9420-df74-4dfa-a8bf-256f65d20986' class='xr-section-summary-in' type='checkbox'  ><label for='section-47ee9420-df74-4dfa-a8bf-256f65d20986' class='xr-section-summary' >Data variables: <span>(16)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>10m_u_component_of_wind</span></div><div class='xr-var-dims'>(time, prediction_timedelta, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(4, 1, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-f9514151-51dd-4b8c-b1e3-9689bccae7d3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f9514151-51dd-4b8c-b1e3-9689bccae7d3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d7f3e70f-cd59-4176-9022-8a8014a9a516' class='xr-var-data-in' type='checkbox'><label for='data-d7f3e70f-cd59-4176-9022-8a8014a9a516' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>10 metre U wind component</dd><dt><span>short_name :</span></dt><dd>u10</dd><dt><span>standard_name :</span></dt><dd>unknown</dd><dt><span>units :</span></dt><dd>m s**-1</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 1.60 GiB </td>\n",
       "                        <td> 32.00 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
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       "                        <td> (5114, 41, 64, 32) </td>\n",
       "                        <td> (4, 1, 64, 32) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 52439 chunks in 2 graph layers </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>10m_v_component_of_wind</span></div><div class='xr-var-dims'>(time, prediction_timedelta, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(4, 1, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-ad8db35b-05a9-43c5-baa8-5e6bed084c31' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ad8db35b-05a9-43c5-baa8-5e6bed084c31' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d7fbf2c7-57b1-4ff9-95c1-a34cfd8e0b4b' class='xr-var-data-in' type='checkbox'><label for='data-d7fbf2c7-57b1-4ff9-95c1-a34cfd8e0b4b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>10 metre V wind component</dd><dt><span>short_name :</span></dt><dd>v10</dd><dt><span>standard_name :</span></dt><dd>unknown</dd><dt><span>units :</span></dt><dd>m s**-1</dd></dl></div><div class='xr-var-data'><table>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>10m_wind_speed</span></div><div class='xr-var-dims'>(time, prediction_timedelta, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(4, 1, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-e3fd174d-34ff-4338-bfca-02d05b87eb2e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e3fd174d-34ff-4338-bfca-02d05b87eb2e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c4fb38aa-e104-41c5-bf12-4e1b63dfb2c7' class='xr-var-data-in' type='checkbox'><label for='data-c4fb38aa-e104-41c5-bf12-4e1b63dfb2c7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>2m_temperature</span></div><div class='xr-var-dims'>(time, prediction_timedelta, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(4, 1, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-ad2c00f2-dac3-4ca2-b211-05a55bc6c105' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ad2c00f2-dac3-4ca2-b211-05a55bc6c105' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4359ff38-ce64-4122-b15a-eb2cd47b6bbc' class='xr-var-data-in' type='checkbox'><label for='data-4359ff38-ce64-4122-b15a-eb2cd47b6bbc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>2 metre temperature</dd><dt><span>short_name :</span></dt><dd>t2m</dd><dt><span>standard_name :</span></dt><dd>unknown</dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                        <th> Bytes </th>\n",
       "                        <td> 1.60 GiB </td>\n",
       "                        <td> 32.00 kiB </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>geopotential</span></div><div class='xr-var-dims'>(time, prediction_timedelta, level, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(4, 1, 13, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-0441f335-cf2f-4b75-9463-5cff73bdc527' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0441f335-cf2f-4b75-9463-5cff73bdc527' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e9899da3-b49b-4452-9ac8-29374a08d60b' class='xr-var-data-in' type='checkbox'><label for='data-e9899da3-b49b-4452-9ac8-29374a08d60b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Geopotential</dd><dt><span>short_name :</span></dt><dd>z</dd><dt><span>standard_name :</span></dt><dd>geopotential</dd><dt><span>units :</span></dt><dd>m**2 s**-2</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                        <th> Bytes </th>\n",
       "                        <td> 1.60 GiB </td>\n",
       "                        <td> 32.00 kiB </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>specific_humidity</span></div><div class='xr-var-dims'>(time, prediction_timedelta, level, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(4, 1, 13, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-799d3d07-82e7-4645-b89d-b00c1c177ced' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-799d3d07-82e7-4645-b89d-b00c1c177ced' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c338bbbf-714d-4ad7-b4ae-7a6aceed2b5a' class='xr-var-data-in' type='checkbox'><label for='data-c338bbbf-714d-4ad7-b4ae-7a6aceed2b5a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Specific humidity</dd><dt><span>short_name :</span></dt><dd>q</dd><dt><span>standard_name :</span></dt><dd>specific_humidity</dd><dt><span>units :</span></dt><dd>kg kg**-1</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 20.80 GiB </td>\n",
       "                        <td> 416.00 kiB </td>\n",
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       "                        <th> Shape </th>\n",
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       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 52439 chunks in 2 graph layers </td>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 1.60 GiB </td>\n",
       "                        <td> 32.00 kiB </td>\n",
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       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (5114, 41, 64, 32) </td>\n",
       "                        <td> (4, 1, 64, 32) </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temperature</span></div><div class='xr-var-dims'>(time, prediction_timedelta, level, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(4, 1, 13, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-de8baa05-3fef-44a6-8e05-cf2e1cf16fd2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-de8baa05-3fef-44a6-8e05-cf2e1cf16fd2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f3336e8b-0b8d-4bf7-9f2e-fbeeeba71a76' class='xr-var-data-in' type='checkbox'><label for='data-f3336e8b-0b8d-4bf7-9f2e-fbeeeba71a76' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Temperature</dd><dt><span>short_name :</span></dt><dd>t</dd><dt><span>standard_name :</span></dt><dd>air_temperature</dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 20.80 GiB </td>\n",
       "                        <td> 416.00 kiB </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>total_precipitation</span></div><div class='xr-var-dims'>(time, prediction_timedelta, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(4, 1, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-addb677b-83f7-4925-ba77-8fe15df17110' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-addb677b-83f7-4925-ba77-8fe15df17110' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bd09cd1f-2530-4eb1-830f-49e9c000f11e' class='xr-var-data-in' type='checkbox'><label for='data-bd09cd1f-2530-4eb1-830f-49e9c000f11e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Total precipitation</dd><dt><span>short_name :</span></dt><dd>tp</dd><dt><span>standard_name :</span></dt><dd>unknown</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                        <th> Bytes </th>\n",
       "                        <td> 1.60 GiB </td>\n",
       "                        <td> 32.00 kiB </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>total_precipitation_24hr</span></div><div class='xr-var-dims'>(time, prediction_timedelta, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(4, 1, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-bab6ef58-7002-433a-a6b1-d41289b3da09' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-bab6ef58-7002-433a-a6b1-d41289b3da09' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e94e774c-28b3-4765-a1d4-892e2e2a95eb' class='xr-var-data-in' type='checkbox'><label for='data-e94e774c-28b3-4765-a1d4-892e2e2a95eb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>total_precipitation_6hr</span></div><div class='xr-var-dims'>(time, prediction_timedelta, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(4, 1, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-89e9c780-4142-431e-bae3-8970f06d2441' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-89e9c780-4142-431e-bae3-8970f06d2441' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fd2ff151-01de-4bf3-9463-bb16ca566c33' class='xr-var-data-in' type='checkbox'><label for='data-fd2ff151-01de-4bf3-9463-bb16ca566c33' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
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       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
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       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 1.60 GiB </td>\n",
       "                        <td> 32.00 kiB </td>\n",
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       "                    <tr>\n",
       "                        <th> Shape </th>\n",
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       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 20.80 GiB </td>\n",
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       "                        <th> Shape </th>\n",
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       "                        <th> Dask graph </th>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 20.80 GiB </td>\n",
       "                        <td> 416.00 kiB </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vertical_velocity</span></div><div class='xr-var-dims'>(time, prediction_timedelta, level, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(4, 1, 13, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-41f83bd8-6fae-4817-bc56-7d6c4f321122' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-41f83bd8-6fae-4817-bc56-7d6c4f321122' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b190f042-918b-440c-a7ea-34a88e71930b' class='xr-var-data-in' type='checkbox'><label for='data-b190f042-918b-440c-a7ea-34a88e71930b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Vertical velocity</dd><dt><span>short_name :</span></dt><dd>w</dd><dt><span>standard_name :</span></dt><dd>lagrangian_tendency_of_air_pressure</dd><dt><span>units :</span></dt><dd>Pa s**-1</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 20.80 GiB </td>\n",
       "                        <td> 416.00 kiB </td>\n",
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       "                        <th> Bytes </th>\n",
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       "        -70.31249999999999,  -64.68750000000001,            -59.0625,\n",
       "                  -53.4375,            -47.8125,            -42.1875,\n",
       "                  -36.5625, -30.937499999999996, -25.312500000000004,\n",
       "       -19.687499999999996, -14.062499999999991,  -8.437499999999996,\n",
       "        -2.812500000000003,   2.812500000000003,   8.437500000000009,\n",
       "        14.062500000000004,  19.687499999999996,  25.312500000000004,\n",
       "         30.93750000000001,  36.562499999999986,             42.1875,\n",
       "                   47.8125,             53.4375,  59.062500000000014,\n",
       "         64.68750000000001,             70.3125,             75.9375,\n",
       "         81.56249999999997,   87.18750000000003],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;latitude&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>level</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-e43955dc-28e1-42dc-800f-de1efe6d5c12' class='xr-index-data-in' type='checkbox'/><label for='index-e43955dc-28e1-42dc-800f-de1efe6d5c12' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([50, 100, 150, 200, 250, 300, 400, 500, 600, 700, 850, 925, 1000], dtype=&#x27;int32&#x27;, name=&#x27;level&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>longitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-08fa9e34-19d7-4f7c-9633-05edef82f5c4' class='xr-index-data-in' type='checkbox'/><label for='index-08fa9e34-19d7-4f7c-9633-05edef82f5c4' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([               0.0,              5.625,              11.25,\n",
       "                   16.875,               22.5,             28.125,\n",
       "                    33.75,             39.375,               45.0,\n",
       "                   50.625,              56.25,  61.87499999999999,\n",
       "                     67.5,             73.125,              78.75,\n",
       "                   84.375,               90.0,             95.625,\n",
       "                   101.25,            106.875,              112.5,\n",
       "                  118.125, 123.74999999999999,            129.375,\n",
       "                    135.0,            140.625,             146.25,\n",
       "                  151.875,              157.5,            163.125,\n",
       "                   168.75,            174.375,              180.0,\n",
       "                  185.625,             191.25,            196.875,\n",
       "                    202.5,            208.125,             213.75,\n",
       "                  219.375,              225.0, 230.62499999999997,\n",
       "                   236.25,            241.875, 247.49999999999997,\n",
       "                  253.125,             258.75,            264.375,\n",
       "                    270.0,            275.625,             281.25,\n",
       "                  286.875,              292.5,            298.125,\n",
       "                   303.75,            309.375,              315.0,\n",
       "                  320.625,             326.25,            331.875,\n",
       "                    337.5,            343.125,             348.75,\n",
       "                  354.375],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;longitude&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>prediction_timedelta</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-6e710f0d-da60-454f-92ea-5053dc488ac7' class='xr-index-data-in' type='checkbox'/><label for='index-6e710f0d-da60-454f-92ea-5053dc488ac7' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(TimedeltaIndex([ &#x27;0 days 00:00:00&#x27;,  &#x27;0 days 06:00:00&#x27;,  &#x27;0 days 12:00:00&#x27;,\n",
       "                 &#x27;0 days 18:00:00&#x27;,  &#x27;1 days 00:00:00&#x27;,  &#x27;1 days 06:00:00&#x27;,\n",
       "                 &#x27;1 days 12:00:00&#x27;,  &#x27;1 days 18:00:00&#x27;,  &#x27;2 days 00:00:00&#x27;,\n",
       "                 &#x27;2 days 06:00:00&#x27;,  &#x27;2 days 12:00:00&#x27;,  &#x27;2 days 18:00:00&#x27;,\n",
       "                 &#x27;3 days 00:00:00&#x27;,  &#x27;3 days 06:00:00&#x27;,  &#x27;3 days 12:00:00&#x27;,\n",
       "                 &#x27;3 days 18:00:00&#x27;,  &#x27;4 days 00:00:00&#x27;,  &#x27;4 days 06:00:00&#x27;,\n",
       "                 &#x27;4 days 12:00:00&#x27;,  &#x27;4 days 18:00:00&#x27;,  &#x27;5 days 00:00:00&#x27;,\n",
       "                 &#x27;5 days 06:00:00&#x27;,  &#x27;5 days 12:00:00&#x27;,  &#x27;5 days 18:00:00&#x27;,\n",
       "                 &#x27;6 days 00:00:00&#x27;,  &#x27;6 days 06:00:00&#x27;,  &#x27;6 days 12:00:00&#x27;,\n",
       "                 &#x27;6 days 18:00:00&#x27;,  &#x27;7 days 00:00:00&#x27;,  &#x27;7 days 06:00:00&#x27;,\n",
       "                 &#x27;7 days 12:00:00&#x27;,  &#x27;7 days 18:00:00&#x27;,  &#x27;8 days 00:00:00&#x27;,\n",
       "                 &#x27;8 days 06:00:00&#x27;,  &#x27;8 days 12:00:00&#x27;,  &#x27;8 days 18:00:00&#x27;,\n",
       "                 &#x27;9 days 00:00:00&#x27;,  &#x27;9 days 06:00:00&#x27;,  &#x27;9 days 12:00:00&#x27;,\n",
       "                 &#x27;9 days 18:00:00&#x27;, &#x27;10 days 00:00:00&#x27;],\n",
       "               dtype=&#x27;timedelta64[ns]&#x27;, name=&#x27;prediction_timedelta&#x27;, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-ed1007bc-2401-4bb3-aaab-28dc225d52db' class='xr-index-data-in' type='checkbox'/><label for='index-ed1007bc-2401-4bb3-aaab-28dc225d52db' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2016-01-01 00:00:00&#x27;, &#x27;2016-01-01 12:00:00&#x27;,\n",
       "               &#x27;2016-01-02 00:00:00&#x27;, &#x27;2016-01-02 12:00:00&#x27;,\n",
       "               &#x27;2016-01-03 00:00:00&#x27;, &#x27;2016-01-03 12:00:00&#x27;,\n",
       "               &#x27;2016-01-04 00:00:00&#x27;, &#x27;2016-01-04 12:00:00&#x27;,\n",
       "               &#x27;2016-01-05 00:00:00&#x27;, &#x27;2016-01-05 12:00:00&#x27;,\n",
       "               ...\n",
       "               &#x27;2022-12-27 00:00:00&#x27;, &#x27;2022-12-27 12:00:00&#x27;,\n",
       "               &#x27;2022-12-28 00:00:00&#x27;, &#x27;2022-12-28 12:00:00&#x27;,\n",
       "               &#x27;2022-12-29 00:00:00&#x27;, &#x27;2022-12-29 12:00:00&#x27;,\n",
       "               &#x27;2022-12-30 00:00:00&#x27;, &#x27;2022-12-30 12:00:00&#x27;,\n",
       "               &#x27;2022-12-31 00:00:00&#x27;, &#x27;2022-12-31 12:00:00&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=5114, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-dfac0d5b-d502-4fae-8515-db4e87e1c1b1' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-dfac0d5b-d502-4fae-8515-db4e87e1c1b1' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:                   (time: 5114, prediction_timedelta: 41,\n",
       "                               longitude: 64, latitude: 32, level: 13)\n",
       "Coordinates:\n",
       "  * latitude                  (latitude) float64 -87.19 -81.56 ... 81.56 87.19\n",
       "  * level                     (level) int32 50 100 150 200 ... 700 850 925 1000\n",
       "  * longitude                 (longitude) float64 0.0 5.625 ... 348.8 354.4\n",
       "  * prediction_timedelta      (prediction_timedelta) timedelta64[ns] 00:00:00...\n",
       "  * time                      (time) datetime64[ns] 2016-01-01 ... 2022-12-31...\n",
       "Data variables: (12/16)\n",
       "    10m_u_component_of_wind   (time, prediction_timedelta, longitude, latitude) float32 dask.array<chunksize=(4, 1, 64, 32), meta=np.ndarray>\n",
       "    10m_v_component_of_wind   (time, prediction_timedelta, longitude, latitude) float32 dask.array<chunksize=(4, 1, 64, 32), meta=np.ndarray>\n",
       "    10m_wind_speed            (time, prediction_timedelta, longitude, latitude) float32 dask.array<chunksize=(4, 1, 64, 32), meta=np.ndarray>\n",
       "    2m_temperature            (time, prediction_timedelta, longitude, latitude) float32 dask.array<chunksize=(4, 1, 64, 32), meta=np.ndarray>\n",
       "    geopotential              (time, prediction_timedelta, level, longitude, latitude) float32 dask.array<chunksize=(4, 1, 13, 64, 32), meta=np.ndarray>\n",
       "    mean_sea_level_pressure   (time, prediction_timedelta, longitude, latitude) float32 dask.array<chunksize=(4, 1, 64, 32), meta=np.ndarray>\n",
       "    ...                        ...\n",
       "    total_precipitation_24hr  (time, prediction_timedelta, longitude, latitude) float32 dask.array<chunksize=(4, 1, 64, 32), meta=np.ndarray>\n",
       "    total_precipitation_6hr   (time, prediction_timedelta, longitude, latitude) float32 dask.array<chunksize=(4, 1, 64, 32), meta=np.ndarray>\n",
       "    u_component_of_wind       (time, prediction_timedelta, level, longitude, latitude) float32 dask.array<chunksize=(4, 1, 13, 64, 32), meta=np.ndarray>\n",
       "    v_component_of_wind       (time, prediction_timedelta, level, longitude, latitude) float32 dask.array<chunksize=(4, 1, 13, 64, 32), meta=np.ndarray>\n",
       "    vertical_velocity         (time, prediction_timedelta, level, longitude, latitude) float32 dask.array<chunksize=(4, 1, 13, 64, 32), meta=np.ndarray>\n",
       "    wind_speed                (time, prediction_timedelta, level, longitude, latitude) float32 dask.array<chunksize=(4, 1, 13, 64, 32), meta=np.ndarray>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xr.open_zarr(forecast_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "cd35d83d-7d6b-4475-9509-3639274fec99",
   "metadata": {},
   "outputs": [
    {
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       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "}\n",
       "\n",
       "html[theme=dark],\n",
       "body[data-theme=dark],\n",
       "body.vscode-dark {\n",
       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
       "  --xr-border-color: #1F1F1F;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block !important;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:                                           (time: 92044,\n",
       "                                                       longitude: 64,\n",
       "                                                       latitude: 32, level: 13)\n",
       "Coordinates:\n",
       "  * latitude                                          (latitude) float64 -87....\n",
       "  * level                                             (level) int64 50 ... 1000\n",
       "  * longitude                                         (longitude) float64 0.0...\n",
       "  * time                                              (time) datetime64[ns] 1...\n",
       "Data variables: (12/38)\n",
       "    10m_u_component_of_wind                           (time, longitude, latitude) float32 dask.array&lt;chunksize=(100, 64, 32), meta=np.ndarray&gt;\n",
       "    10m_v_component_of_wind                           (time, longitude, latitude) float32 dask.array&lt;chunksize=(100, 64, 32), meta=np.ndarray&gt;\n",
       "    10m_wind_speed                                    (time, longitude, latitude) float32 dask.array&lt;chunksize=(100, 64, 32), meta=np.ndarray&gt;\n",
       "    2m_temperature                                    (time, longitude, latitude) float32 dask.array&lt;chunksize=(100, 64, 32), meta=np.ndarray&gt;\n",
       "    angle_of_sub_gridscale_orography                  (longitude, latitude) float32 dask.array&lt;chunksize=(64, 32), meta=np.ndarray&gt;\n",
       "    anisotropy_of_sub_gridscale_orography             (longitude, latitude) float32 dask.array&lt;chunksize=(64, 32), meta=np.ndarray&gt;\n",
       "    ...                                                ...\n",
       "    type_of_high_vegetation                           (longitude, latitude) float32 dask.array&lt;chunksize=(64, 32), meta=np.ndarray&gt;\n",
       "    type_of_low_vegetation                            (longitude, latitude) float32 dask.array&lt;chunksize=(64, 32), meta=np.ndarray&gt;\n",
       "    u_component_of_wind                               (time, level, longitude, latitude) float32 dask.array&lt;chunksize=(100, 13, 64, 32), meta=np.ndarray&gt;\n",
       "    v_component_of_wind                               (time, level, longitude, latitude) float32 dask.array&lt;chunksize=(100, 13, 64, 32), meta=np.ndarray&gt;\n",
       "    vertical_velocity                                 (time, level, longitude, latitude) float32 dask.array&lt;chunksize=(100, 13, 64, 32), meta=np.ndarray&gt;\n",
       "    wind_speed                                        (time, level, longitude, latitude) float32 dask.array&lt;chunksize=(100, 13, 64, 32), meta=np.ndarray&gt;</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-676fc099-6720-431a-a245-395323c7f5a6' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-676fc099-6720-431a-a245-395323c7f5a6' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 92044</li><li><span class='xr-has-index'>longitude</span>: 64</li><li><span class='xr-has-index'>latitude</span>: 32</li><li><span class='xr-has-index'>level</span>: 13</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-bad77aff-5d25-41d8-a375-01c34034713b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-bad77aff-5d25-41d8-a375-01c34034713b' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>latitude</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-87.19 -81.56 ... 81.56 87.19</div><input id='attrs-2b51072f-830a-4619-b1be-25cd3bfc9287' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2b51072f-830a-4619-b1be-25cd3bfc9287' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dd1bcf8c-4c5c-495a-ba5f-b7149c369dcc' class='xr-var-data-in' type='checkbox'><label for='data-dd1bcf8c-4c5c-495a-ba5f-b7149c369dcc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-87.1875, -81.5625, -75.9375, -70.3125, -64.6875, -59.0625, -53.4375,\n",
       "       -47.8125, -42.1875, -36.5625, -30.9375, -25.3125, -19.6875, -14.0625,\n",
       "        -8.4375,  -2.8125,   2.8125,   8.4375,  14.0625,  19.6875,  25.3125,\n",
       "        30.9375,  36.5625,  42.1875,  47.8125,  53.4375,  59.0625,  64.6875,\n",
       "        70.3125,  75.9375,  81.5625,  87.1875])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>level</span></div><div class='xr-var-dims'>(level)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>50 100 150 200 ... 700 850 925 1000</div><input id='attrs-4df77667-bb95-4017-98eb-a84f62d591da' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4df77667-bb95-4017-98eb-a84f62d591da' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4e53c1a7-f0f9-48d7-a7a7-d717a14fb5b3' class='xr-var-data-in' type='checkbox'><label for='data-4e53c1a7-f0f9-48d7-a7a7-d717a14fb5b3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  50,  100,  150,  200,  250,  300,  400,  500,  600,  700,  850,  925,\n",
       "       1000])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>longitude</span></div><div class='xr-var-dims'>(longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 5.625 11.25 ... 348.8 354.4</div><input id='attrs-19f952aa-1d14-4344-acbd-bb219667cecf' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-19f952aa-1d14-4344-acbd-bb219667cecf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f05bd593-80d2-4935-8585-3b57cff5b85a' class='xr-var-data-in' type='checkbox'><label for='data-f05bd593-80d2-4935-8585-3b57cff5b85a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0.   ,   5.625,  11.25 ,  16.875,  22.5  ,  28.125,  33.75 ,  39.375,\n",
       "        45.   ,  50.625,  56.25 ,  61.875,  67.5  ,  73.125,  78.75 ,  84.375,\n",
       "        90.   ,  95.625, 101.25 , 106.875, 112.5  , 118.125, 123.75 , 129.375,\n",
       "       135.   , 140.625, 146.25 , 151.875, 157.5  , 163.125, 168.75 , 174.375,\n",
       "       180.   , 185.625, 191.25 , 196.875, 202.5  , 208.125, 213.75 , 219.375,\n",
       "       225.   , 230.625, 236.25 , 241.875, 247.5  , 253.125, 258.75 , 264.375,\n",
       "       270.   , 275.625, 281.25 , 286.875, 292.5  , 298.125, 303.75 , 309.375,\n",
       "       315.   , 320.625, 326.25 , 331.875, 337.5  , 343.125, 348.75 , 354.375])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1959-01-01 ... 2021-12-31T18:00:00</div><input id='attrs-4e8270fd-cb52-4042-ba57-4319c5d16795' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4e8270fd-cb52-4042-ba57-4319c5d16795' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-216be464-2b29-4975-94da-52a44ac1b685' class='xr-var-data-in' type='checkbox'><label for='data-216be464-2b29-4975-94da-52a44ac1b685' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;1959-01-01T00:00:00.000000000&#x27;, &#x27;1959-01-01T06:00:00.000000000&#x27;,\n",
       "       &#x27;1959-01-01T12:00:00.000000000&#x27;, ..., &#x27;2021-12-31T06:00:00.000000000&#x27;,\n",
       "       &#x27;2021-12-31T12:00:00.000000000&#x27;, &#x27;2021-12-31T18:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-df0c794a-db7b-4de2-bd1c-d5007d686dab' class='xr-section-summary-in' type='checkbox'  ><label for='section-df0c794a-db7b-4de2-bd1c-d5007d686dab' class='xr-section-summary' >Data variables: <span>(38)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>10m_u_component_of_wind</span></div><div class='xr-var-dims'>(time, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(100, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-5affe1a0-3828-4d5b-bcbb-25f0b50063e2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5affe1a0-3828-4d5b-bcbb-25f0b50063e2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-980da0b1-af3d-45ad-9f6b-276ca0fbe7f1' class='xr-var-data-in' type='checkbox'><label for='data-980da0b1-af3d-45ad-9f6b-276ca0fbe7f1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>10 metre U wind component</dd><dt><span>short_name :</span></dt><dd>u10</dd><dt><span>units :</span></dt><dd>m s**-1</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                        <td> 719.09 MiB </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>angle_of_sub_gridscale_orography</span></div><div class='xr-var-dims'>(longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(64, 32), meta=np.ndarray&gt;</div><input id='attrs-56fec95a-dce0-4cdf-b94c-8b6f4b053836' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-56fec95a-dce0-4cdf-b94c-8b6f4b053836' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5183deb7-756f-4363-907d-2a7796b3bb89' class='xr-var-data-in' type='checkbox'><label for='data-5183deb7-756f-4363-907d-2a7796b3bb89' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Angle of sub-gridscale orography</dd><dt><span>short_name :</span></dt><dd>anor</dd><dt><span>units :</span></dt><dd>radians</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                    <tr>\n",
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       "                        <td> (64, 32) </td>\n",
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       "                        <th> Data type </th>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 8.00 kiB </td>\n",
       "                        <td> 8.00 kiB </td>\n",
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       "                        <td> (64, 32) </td>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
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       "                        <td> (100, 13, 64, 32) </td>\n",
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       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>land_sea_mask</span></div><div class='xr-var-dims'>(longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(64, 32), meta=np.ndarray&gt;</div><input id='attrs-bfbc0faf-0347-4d82-8bbc-ca049a014e68' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bfbc0faf-0347-4d82-8bbc-ca049a014e68' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-22960b3c-e998-45c3-b0cd-953a3fa4a3f7' class='xr-var-data-in' type='checkbox'><label for='data-22960b3c-e998-45c3-b0cd-953a3fa4a3f7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Land-sea mask</dd><dt><span>short_name :</span></dt><dd>lsm</dd><dt><span>standard_name :</span></dt><dd>land_binary_mask</dd><dt><span>units :</span></dt><dd>(0 - 1)</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 8.00 kiB </td>\n",
       "                        <td> 8.00 kiB </td>\n",
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       "                    <tr>\n",
       "                        <th> Shape </th>\n",
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       "                        <td> (64, 32) </td>\n",
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       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>low_vegetation_cover</span></div><div class='xr-var-dims'>(longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(64, 32), meta=np.ndarray&gt;</div><input id='attrs-f31cc21d-4818-4fb6-b1cb-f3e7f37ba015' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f31cc21d-4818-4fb6-b1cb-f3e7f37ba015' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2c4d3f29-6951-4a69-917c-cc0535cd4bc5' class='xr-var-data-in' type='checkbox'><label for='data-2c4d3f29-6951-4a69-917c-cc0535cd4bc5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Low vegetation cover</dd><dt><span>short_name :</span></dt><dd>cvl</dd><dt><span>units :</span></dt><dd>(0 - 1)</dd></dl></div><div class='xr-var-data'><table>\n",
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       "        <td>\n",
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       "                <tbody>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 8.00 kiB </td>\n",
       "                        <td> 8.00 kiB </td>\n",
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       "                    <tr>\n",
       "                        <th> Shape </th>\n",
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       "                        <td> (64, 32) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 719.09 MiB </td>\n",
       "                        <td> 800.00 kiB </td>\n",
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       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
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       "                        <th> Dask graph </th>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sea_ice_cover</span></div><div class='xr-var-dims'>(time, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(100, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-5403fa64-03ff-4926-8e56-178a5f38c0fc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5403fa64-03ff-4926-8e56-178a5f38c0fc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ff0177cb-09c4-4b66-856d-13eaa6e38a2a' class='xr-var-data-in' type='checkbox'><label for='data-ff0177cb-09c4-4b66-856d-13eaa6e38a2a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Sea ice area fraction</dd><dt><span>short_name :</span></dt><dd>siconc</dd><dt><span>standard_name :</span></dt><dd>sea_ice_area_fraction</dd><dt><span>units :</span></dt><dd>(0 - 1)</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 719.09 MiB </td>\n",
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       "                        <th> Shape </th>\n",
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       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 921 chunks in 2 graph layers </td>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
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       "                        <th> Data type </th>\n",
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       "                <tbody>\n",
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       "                    <tr>\n",
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       "                        <td> 8.00 kiB </td>\n",
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       "                        <td> (64, 32) </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>specific_humidity</span></div><div class='xr-var-dims'>(time, level, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(100, 13, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-ded3228e-a4ea-4d67-8b1b-bdaa619b611b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ded3228e-a4ea-4d67-8b1b-bdaa619b611b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4c712242-0f93-45f0-a150-e37dd63e69fe' class='xr-var-data-in' type='checkbox'><label for='data-4c712242-0f93-45f0-a150-e37dd63e69fe' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Specific humidity</dd><dt><span>short_name :</span></dt><dd>q</dd><dt><span>standard_name :</span></dt><dd>specific_humidity</dd><dt><span>units :</span></dt><dd>kg kg**-1</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                    <tr>\n",
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       "                        <th> Bytes </th>\n",
       "                        <td> 719.09 MiB </td>\n",
       "                        <td> 800.00 kiB </td>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 719.09 MiB </td>\n",
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       "                        <th> Bytes </th>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 719.09 MiB </td>\n",
       "                        <td> 800.00 kiB </td>\n",
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       "                        <th> Dask graph </th>\n",
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       "                        <th> Data type </th>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>total_precipitation_12hr</span></div><div class='xr-var-dims'>(time, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(100, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-2ee0382d-1d9a-42bc-8976-378ff8d4f3bf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2ee0382d-1d9a-42bc-8976-378ff8d4f3bf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3ae05f36-c14d-4b93-9b20-4c852da402c9' class='xr-var-data-in' type='checkbox'><label for='data-3ae05f36-c14d-4b93-9b20-4c852da402c9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Total precipitation</dd><dt><span>short_name :</span></dt><dd>tp</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 719.09 MiB </td>\n",
       "                        <td> 800.00 kiB </td>\n",
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       "                        <td> (100, 64, 32) </td>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 719.09 MiB </td>\n",
       "                        <td> 800.00 kiB </td>\n",
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       "                        <th> Shape </th>\n",
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       "                        <td> (100, 64, 32) </td>\n",
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       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 719.09 MiB </td>\n",
       "                        <td> 800.00 kiB </td>\n",
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       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (92044, 64, 32) </td>\n",
       "                        <td> (100, 64, 32) </td>\n",
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       "                        <th> Dask graph </th>\n",
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       "                        <th> Data type </th>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>type_of_high_vegetation</span></div><div class='xr-var-dims'>(longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(64, 32), meta=np.ndarray&gt;</div><input id='attrs-f4803fd1-f0f1-4be5-a442-3ce4fa170c4f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f4803fd1-f0f1-4be5-a442-3ce4fa170c4f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0a2a8f0c-6be0-42c0-aa08-b16991cda789' class='xr-var-data-in' type='checkbox'><label for='data-0a2a8f0c-6be0-42c0-aa08-b16991cda789' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Type of high vegetation</dd><dt><span>short_name :</span></dt><dd>tvh</dd><dt><span>units :</span></dt><dd>~</dd></dl></div><div class='xr-var-data'><table>\n",
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       "        <td>\n",
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       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 8.00 kiB </td>\n",
       "                        <td> 8.00 kiB </td>\n",
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       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (64, 32) </td>\n",
       "                        <td> (64, 32) </td>\n",
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       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>type_of_low_vegetation</span></div><div class='xr-var-dims'>(longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(64, 32), meta=np.ndarray&gt;</div><input id='attrs-0575b304-b891-486b-8639-26f472fc8c6a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0575b304-b891-486b-8639-26f472fc8c6a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-02bb95d7-7edb-495c-9169-5c2ee378e052' class='xr-var-data-in' type='checkbox'><label for='data-02bb95d7-7edb-495c-9169-5c2ee378e052' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Type of low vegetation</dd><dt><span>short_name :</span></dt><dd>tvl</dd><dt><span>units :</span></dt><dd>~</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                <tbody>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 8.00 kiB </td>\n",
       "                        <td> 8.00 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (64, 32) </td>\n",
       "                        <td> (64, 32) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>u_component_of_wind</span></div><div class='xr-var-dims'>(time, level, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(100, 13, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-c3a7181d-08ce-41fa-acba-62c8de4027cf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c3a7181d-08ce-41fa-acba-62c8de4027cf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-48b7eee3-2430-41f9-b4c3-00af61e24c48' class='xr-var-data-in' type='checkbox'><label for='data-48b7eee3-2430-41f9-b4c3-00af61e24c48' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>U component of wind</dd><dt><span>short_name :</span></dt><dd>u</dd><dt><span>standard_name :</span></dt><dd>eastward_wind</dd><dt><span>units :</span></dt><dd>m s**-1</dd></dl></div><div class='xr-var-data'><table>\n",
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       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
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       "                    <tr>\n",
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       "                <tbody>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 9.13 GiB </td>\n",
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       "                    <tr>\n",
       "                        <th> Shape </th>\n",
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       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
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       "                    <tr>\n",
       "                        <th> Shape </th>\n",
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       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 921 chunks in 2 graph layers </td>\n",
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       "                        <th> Data type </th>\n",
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       "                        <th> Bytes </th>\n",
       "                        <td> 9.13 GiB </td>\n",
       "                        <td> 10.16 MiB </td>\n",
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       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-b39ff92a-0399-44d6-a5f9-3a517522aec1' class='xr-section-summary-in' type='checkbox'  ><label for='section-b39ff92a-0399-44d6-a5f9-3a517522aec1' class='xr-section-summary' >Indexes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>latitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-a9894a9b-b200-4a15-bbf7-27205f44557f' class='xr-index-data-in' type='checkbox'/><label for='index-a9894a9b-b200-4a15-bbf7-27205f44557f' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ -87.18750000000003,  -81.56250000000001,            -75.9375,\n",
       "        -70.31249999999999,  -64.68750000000001,            -59.0625,\n",
       "                  -53.4375,            -47.8125,            -42.1875,\n",
       "                  -36.5625, -30.937499999999996, -25.312500000000004,\n",
       "       -19.687499999999996, -14.062499999999991,  -8.437499999999996,\n",
       "        -2.812500000000003,   2.812500000000003,   8.437500000000009,\n",
       "        14.062500000000004,  19.687499999999996,  25.312500000000004,\n",
       "         30.93750000000001,  36.562499999999986,             42.1875,\n",
       "                   47.8125,             53.4375,  59.062500000000014,\n",
       "         64.68750000000001,             70.3125,             75.9375,\n",
       "         81.56249999999997,   87.18750000000003],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;latitude&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>level</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-1ca2a692-9f2a-4945-b6be-72c829d60881' class='xr-index-data-in' type='checkbox'/><label for='index-1ca2a692-9f2a-4945-b6be-72c829d60881' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([50, 100, 150, 200, 250, 300, 400, 500, 600, 700, 850, 925, 1000], dtype=&#x27;int64&#x27;, name=&#x27;level&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>longitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-9ed06e55-a50e-4b47-a891-09f0849cde22' class='xr-index-data-in' type='checkbox'/><label for='index-9ed06e55-a50e-4b47-a891-09f0849cde22' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([               0.0,              5.625,              11.25,\n",
       "                   16.875,               22.5,             28.125,\n",
       "                    33.75,             39.375,               45.0,\n",
       "                   50.625,              56.25,  61.87499999999999,\n",
       "                     67.5,             73.125,              78.75,\n",
       "                   84.375,               90.0,             95.625,\n",
       "                   101.25,            106.875,              112.5,\n",
       "                  118.125, 123.74999999999999,            129.375,\n",
       "                    135.0,            140.625,             146.25,\n",
       "                  151.875,              157.5,            163.125,\n",
       "                   168.75,            174.375,              180.0,\n",
       "                  185.625,             191.25,            196.875,\n",
       "                    202.5,            208.125,             213.75,\n",
       "                  219.375,              225.0, 230.62499999999997,\n",
       "                   236.25,            241.875, 247.49999999999997,\n",
       "                  253.125,             258.75,            264.375,\n",
       "                    270.0,            275.625,             281.25,\n",
       "                  286.875,              292.5,            298.125,\n",
       "                   303.75,            309.375,              315.0,\n",
       "                  320.625,             326.25,            331.875,\n",
       "                    337.5,            343.125,             348.75,\n",
       "                  354.375],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;longitude&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-f79c19d9-ba40-4c7b-8814-17a8a89df935' class='xr-index-data-in' type='checkbox'/><label for='index-f79c19d9-ba40-4c7b-8814-17a8a89df935' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;1959-01-01 00:00:00&#x27;, &#x27;1959-01-01 06:00:00&#x27;,\n",
       "               &#x27;1959-01-01 12:00:00&#x27;, &#x27;1959-01-01 18:00:00&#x27;,\n",
       "               &#x27;1959-01-02 00:00:00&#x27;, &#x27;1959-01-02 06:00:00&#x27;,\n",
       "               &#x27;1959-01-02 12:00:00&#x27;, &#x27;1959-01-02 18:00:00&#x27;,\n",
       "               &#x27;1959-01-03 00:00:00&#x27;, &#x27;1959-01-03 06:00:00&#x27;,\n",
       "               ...\n",
       "               &#x27;2021-12-29 12:00:00&#x27;, &#x27;2021-12-29 18:00:00&#x27;,\n",
       "               &#x27;2021-12-30 00:00:00&#x27;, &#x27;2021-12-30 06:00:00&#x27;,\n",
       "               &#x27;2021-12-30 12:00:00&#x27;, &#x27;2021-12-30 18:00:00&#x27;,\n",
       "               &#x27;2021-12-31 00:00:00&#x27;, &#x27;2021-12-31 06:00:00&#x27;,\n",
       "               &#x27;2021-12-31 12:00:00&#x27;, &#x27;2021-12-31 18:00:00&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=92044, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f35fa2b9-0bc5-46ba-8e78-33c75b9448f1' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-f35fa2b9-0bc5-46ba-8e78-33c75b9448f1' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:                                           (time: 92044,\n",
       "                                                       longitude: 64,\n",
       "                                                       latitude: 32, level: 13)\n",
       "Coordinates:\n",
       "  * latitude                                          (latitude) float64 -87....\n",
       "  * level                                             (level) int64 50 ... 1000\n",
       "  * longitude                                         (longitude) float64 0.0...\n",
       "  * time                                              (time) datetime64[ns] 1...\n",
       "Data variables: (12/38)\n",
       "    10m_u_component_of_wind                           (time, longitude, latitude) float32 dask.array<chunksize=(100, 64, 32), meta=np.ndarray>\n",
       "    10m_v_component_of_wind                           (time, longitude, latitude) float32 dask.array<chunksize=(100, 64, 32), meta=np.ndarray>\n",
       "    10m_wind_speed                                    (time, longitude, latitude) float32 dask.array<chunksize=(100, 64, 32), meta=np.ndarray>\n",
       "    2m_temperature                                    (time, longitude, latitude) float32 dask.array<chunksize=(100, 64, 32), meta=np.ndarray>\n",
       "    angle_of_sub_gridscale_orography                  (longitude, latitude) float32 dask.array<chunksize=(64, 32), meta=np.ndarray>\n",
       "    anisotropy_of_sub_gridscale_orography             (longitude, latitude) float32 dask.array<chunksize=(64, 32), meta=np.ndarray>\n",
       "    ...                                                ...\n",
       "    type_of_high_vegetation                           (longitude, latitude) float32 dask.array<chunksize=(64, 32), meta=np.ndarray>\n",
       "    type_of_low_vegetation                            (longitude, latitude) float32 dask.array<chunksize=(64, 32), meta=np.ndarray>\n",
       "    u_component_of_wind                               (time, level, longitude, latitude) float32 dask.array<chunksize=(100, 13, 64, 32), meta=np.ndarray>\n",
       "    v_component_of_wind                               (time, level, longitude, latitude) float32 dask.array<chunksize=(100, 13, 64, 32), meta=np.ndarray>\n",
       "    vertical_velocity                                 (time, level, longitude, latitude) float32 dask.array<chunksize=(100, 13, 64, 32), meta=np.ndarray>\n",
       "    wind_speed                                        (time, level, longitude, latitude) float32 dask.array<chunksize=(100, 13, 64, 32), meta=np.ndarray>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xr.open_zarr(obs_path)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "4ed2994f-bb1f-4e6c-8aa7-df7233b436dc",
   "metadata": {},
   "source": [
    "Some metrics like the ACC also require a climatology file. Refer to the paper for details on how this is computed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2c321485-1ab7-48a0-a3e2-ccd561ff3fbf",
   "metadata": {},
   "outputs": [
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       ".xr-var-name,\n",
       ".xr-var-dims,\n",
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       ".xr-var-dtype:hover,\n",
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       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
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       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
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       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
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       "  padding: 0;\n",
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       "  grid-column: 1;\n",
       "}\n",
       "\n",
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       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
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       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:                                      (hour: 4, dayofyear: 366,\n",
       "                                                  longitude: 64, latitude: 32,\n",
       "                                                  level: 13)\n",
       "Coordinates:\n",
       "  * dayofyear                                    (dayofyear) int64 1 2 ... 366\n",
       "  * hour                                         (hour) int64 0 6 12 18\n",
       "  * latitude                                     (latitude) float64 -87.19 .....\n",
       "  * level                                        (level) int64 50 100 ... 1000\n",
       "  * longitude                                    (longitude) float64 0.0 ... ...\n",
       "Data variables: (12/28)\n",
       "    10m_u_component_of_wind                      (hour, dayofyear, longitude, latitude) float32 dask.array&lt;chunksize=(4, 366, 64, 32), meta=np.ndarray&gt;\n",
       "    10m_v_component_of_wind                      (hour, dayofyear, longitude, latitude) float32 dask.array&lt;chunksize=(4, 366, 64, 32), meta=np.ndarray&gt;\n",
       "    10m_wind_speed                               (hour, dayofyear, longitude, latitude) float32 dask.array&lt;chunksize=(4, 366, 64, 32), meta=np.ndarray&gt;\n",
       "    2m_temperature                               (hour, dayofyear, longitude, latitude) float32 dask.array&lt;chunksize=(4, 366, 64, 32), meta=np.ndarray&gt;\n",
       "    geopotential                                 (hour, dayofyear, level, longitude, latitude) float32 dask.array&lt;chunksize=(4, 366, 13, 64, 32), meta=np.ndarray&gt;\n",
       "    mean_sea_level_pressure                      (hour, dayofyear, longitude, latitude) float32 dask.array&lt;chunksize=(4, 366, 64, 32), meta=np.ndarray&gt;\n",
       "    ...                                           ...\n",
       "    total_precipitation_6hr_seeps_dry_fraction   (hour, dayofyear, longitude, latitude) float32 dask.array&lt;chunksize=(4, 366, 64, 32), meta=np.ndarray&gt;\n",
       "    total_precipitation_6hr_seeps_threshold      (hour, dayofyear, longitude, latitude) float32 dask.array&lt;chunksize=(4, 366, 64, 32), meta=np.ndarray&gt;\n",
       "    u_component_of_wind                          (hour, dayofyear, level, longitude, latitude) float32 dask.array&lt;chunksize=(4, 366, 13, 64, 32), meta=np.ndarray&gt;\n",
       "    v_component_of_wind                          (hour, dayofyear, level, longitude, latitude) float32 dask.array&lt;chunksize=(4, 366, 13, 64, 32), meta=np.ndarray&gt;\n",
       "    vertical_velocity                            (hour, dayofyear, level, longitude, latitude) float32 dask.array&lt;chunksize=(4, 366, 13, 64, 32), meta=np.ndarray&gt;\n",
       "    wind_speed                                   (hour, dayofyear, level, longitude, latitude) float32 dask.array&lt;chunksize=(4, 366, 13, 64, 32), meta=np.ndarray&gt;</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-a07a321c-ad8c-4e9d-bbe3-3bb72f9a1c47' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-a07a321c-ad8c-4e9d-bbe3-3bb72f9a1c47' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>hour</span>: 4</li><li><span class='xr-has-index'>dayofyear</span>: 366</li><li><span class='xr-has-index'>longitude</span>: 64</li><li><span class='xr-has-index'>latitude</span>: 32</li><li><span class='xr-has-index'>level</span>: 13</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-980207ab-01fb-404e-8b77-182bc7b6095e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-980207ab-01fb-404e-8b77-182bc7b6095e' class='xr-section-summary' >Coordinates: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>dayofyear</span></div><div class='xr-var-dims'>(dayofyear)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 3 4 5 6 ... 362 363 364 365 366</div><input id='attrs-47ca5d5e-001f-4ce1-bef8-adb029943a68' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-47ca5d5e-001f-4ce1-bef8-adb029943a68' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-02b777b6-3687-4949-b262-99978b9a161b' class='xr-var-data-in' type='checkbox'><label for='data-02b777b6-3687-4949-b262-99978b9a161b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  1,   2,   3, ..., 364, 365, 366])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>hour</span></div><div class='xr-var-dims'>(hour)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 6 12 18</div><input id='attrs-1f6d3884-ce1e-49d5-99d1-42a093435351' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1f6d3884-ce1e-49d5-99d1-42a093435351' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-73767ed5-e527-474e-9884-0d9253a4a139' class='xr-var-data-in' type='checkbox'><label for='data-73767ed5-e527-474e-9884-0d9253a4a139' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 0,  6, 12, 18])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>latitude</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-87.19 -81.56 ... 81.56 87.19</div><input id='attrs-49dae77d-25ea-49f2-a2eb-23c50bc92167' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-49dae77d-25ea-49f2-a2eb-23c50bc92167' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-12c9c9cf-0d89-4a85-9506-62f94cba67a3' class='xr-var-data-in' type='checkbox'><label for='data-12c9c9cf-0d89-4a85-9506-62f94cba67a3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-87.1875, -81.5625, -75.9375, -70.3125, -64.6875, -59.0625, -53.4375,\n",
       "       -47.8125, -42.1875, -36.5625, -30.9375, -25.3125, -19.6875, -14.0625,\n",
       "        -8.4375,  -2.8125,   2.8125,   8.4375,  14.0625,  19.6875,  25.3125,\n",
       "        30.9375,  36.5625,  42.1875,  47.8125,  53.4375,  59.0625,  64.6875,\n",
       "        70.3125,  75.9375,  81.5625,  87.1875])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>level</span></div><div class='xr-var-dims'>(level)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>50 100 150 200 ... 700 850 925 1000</div><input id='attrs-afedcd48-de82-453a-a658-7bb58e7ffbac' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-afedcd48-de82-453a-a658-7bb58e7ffbac' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d5260f83-aae9-45a2-a3f6-817df555e95e' class='xr-var-data-in' type='checkbox'><label for='data-d5260f83-aae9-45a2-a3f6-817df555e95e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  50,  100,  150,  200,  250,  300,  400,  500,  600,  700,  850,  925,\n",
       "       1000])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>longitude</span></div><div class='xr-var-dims'>(longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 5.625 11.25 ... 348.8 354.4</div><input id='attrs-cf6c1df6-07e7-4506-b1b1-648774f5ab6d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cf6c1df6-07e7-4506-b1b1-648774f5ab6d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9b4e6ea1-bbe4-467a-99d1-0d0bc1c04804' class='xr-var-data-in' type='checkbox'><label for='data-9b4e6ea1-bbe4-467a-99d1-0d0bc1c04804' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0.   ,   5.625,  11.25 ,  16.875,  22.5  ,  28.125,  33.75 ,  39.375,\n",
       "        45.   ,  50.625,  56.25 ,  61.875,  67.5  ,  73.125,  78.75 ,  84.375,\n",
       "        90.   ,  95.625, 101.25 , 106.875, 112.5  , 118.125, 123.75 , 129.375,\n",
       "       135.   , 140.625, 146.25 , 151.875, 157.5  , 163.125, 168.75 , 174.375,\n",
       "       180.   , 185.625, 191.25 , 196.875, 202.5  , 208.125, 213.75 , 219.375,\n",
       "       225.   , 230.625, 236.25 , 241.875, 247.5  , 253.125, 258.75 , 264.375,\n",
       "       270.   , 275.625, 281.25 , 286.875, 292.5  , 298.125, 303.75 , 309.375,\n",
       "       315.   , 320.625, 326.25 , 331.875, 337.5  , 343.125, 348.75 , 354.375])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a4b95812-7bea-4a88-8257-ee565f5b1539' class='xr-section-summary-in' type='checkbox'  ><label for='section-a4b95812-7bea-4a88-8257-ee565f5b1539' class='xr-section-summary' >Data variables: <span>(28)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>10m_u_component_of_wind</span></div><div class='xr-var-dims'>(hour, dayofyear, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(4, 366, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-f96b7578-3909-45ec-a1f3-a29ce31d97ac' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f96b7578-3909-45ec-a1f3-a29ce31d97ac' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-635d8455-53fa-4065-8af2-8695c9b5762c' class='xr-var-data-in' type='checkbox'><label for='data-635d8455-53fa-4065-8af2-8695c9b5762c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>10 metre U wind component</dd><dt><span>short_name :</span></dt><dd>u10</dd><dt><span>units :</span></dt><dd>m s**-1</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
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       "                        <th> Bytes </th>\n",
       "                        <td> 11.44 MiB </td>\n",
       "                        <td> 11.44 MiB </td>\n",
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       "                        <td> (4, 366, 64, 32) </td>\n",
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       "                        <th> Bytes </th>\n",
       "                        <td> 11.44 MiB </td>\n",
       "                        <td> 11.44 MiB </td>\n",
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       "                        <th> Shape </th>\n",
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       "                        <td> (4, 366, 64, 32) </td>\n",
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       "                        <th> Data type </th>\n",
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       "                        <td> (4, 366, 64, 32) </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>wind_speed</span></div><div class='xr-var-dims'>(hour, dayofyear, level, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(4, 366, 13, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-2b01471f-1399-4b82-a559-c439aca46d39' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2b01471f-1399-4b82-a559-c439aca46d39' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5a77555d-fd38-4f52-9271-972b8f57c698' class='xr-var-data-in' type='checkbox'><label for='data-5a77555d-fd38-4f52-9271-972b8f57c698' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
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       "                        <td> 148.69 MiB </td>\n",
       "                        <td> 148.69 MiB </td>\n",
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       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-cfb28c00-891d-4e5e-a56b-2bffa54394a7' class='xr-section-summary-in' type='checkbox'  ><label for='section-cfb28c00-891d-4e5e-a56b-2bffa54394a7' class='xr-section-summary' >Indexes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>dayofyear</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-6ded5f13-78d2-4e33-9b18-729f9e67ed00' class='xr-index-data-in' type='checkbox'/><label for='index-6ded5f13-78d2-4e33-9b18-729f9e67ed00' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  1,   2,   3,   4,   5,   6,   7,   8,   9,  10,\n",
       "       ...\n",
       "       357, 358, 359, 360, 361, 362, 363, 364, 365, 366],\n",
       "      dtype=&#x27;int64&#x27;, name=&#x27;dayofyear&#x27;, length=366))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>hour</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-440eaff5-8701-46ba-91a6-be3b1d465467' class='xr-index-data-in' type='checkbox'/><label for='index-440eaff5-8701-46ba-91a6-be3b1d465467' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0, 6, 12, 18], dtype=&#x27;int64&#x27;, name=&#x27;hour&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>latitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-e1b42204-d729-44c3-a081-630b1640c265' class='xr-index-data-in' type='checkbox'/><label for='index-e1b42204-d729-44c3-a081-630b1640c265' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ -87.18750000000003,  -81.56250000000001,            -75.9375,\n",
       "        -70.31249999999999,  -64.68750000000001,            -59.0625,\n",
       "                  -53.4375,            -47.8125,            -42.1875,\n",
       "                  -36.5625, -30.937499999999996, -25.312500000000004,\n",
       "       -19.687499999999996, -14.062499999999991,  -8.437499999999996,\n",
       "        -2.812500000000003,   2.812500000000003,   8.437500000000009,\n",
       "        14.062500000000004,  19.687499999999996,  25.312500000000004,\n",
       "         30.93750000000001,  36.562499999999986,             42.1875,\n",
       "                   47.8125,             53.4375,  59.062500000000014,\n",
       "         64.68750000000001,             70.3125,             75.9375,\n",
       "         81.56249999999997,   87.18750000000003],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;latitude&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>level</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-198924bf-ec45-4bea-afc7-1e1fbd10ea38' class='xr-index-data-in' type='checkbox'/><label for='index-198924bf-ec45-4bea-afc7-1e1fbd10ea38' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([50, 100, 150, 200, 250, 300, 400, 500, 600, 700, 850, 925, 1000], dtype=&#x27;int64&#x27;, name=&#x27;level&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>longitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-62a2524f-a57e-438f-ba1b-f22584e645c1' class='xr-index-data-in' type='checkbox'/><label for='index-62a2524f-a57e-438f-ba1b-f22584e645c1' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([               0.0,              5.625,              11.25,\n",
       "                   16.875,               22.5,             28.125,\n",
       "                    33.75,             39.375,               45.0,\n",
       "                   50.625,              56.25,  61.87499999999999,\n",
       "                     67.5,             73.125,              78.75,\n",
       "                   84.375,               90.0,             95.625,\n",
       "                   101.25,            106.875,              112.5,\n",
       "                  118.125, 123.74999999999999,            129.375,\n",
       "                    135.0,            140.625,             146.25,\n",
       "                  151.875,              157.5,            163.125,\n",
       "                   168.75,            174.375,              180.0,\n",
       "                  185.625,             191.25,            196.875,\n",
       "                    202.5,            208.125,             213.75,\n",
       "                  219.375,              225.0, 230.62499999999997,\n",
       "                   236.25,            241.875, 247.49999999999997,\n",
       "                  253.125,             258.75,            264.375,\n",
       "                    270.0,            275.625,             281.25,\n",
       "                  286.875,              292.5,            298.125,\n",
       "                   303.75,            309.375,              315.0,\n",
       "                  320.625,             326.25,            331.875,\n",
       "                    337.5,            343.125,             348.75,\n",
       "                  354.375],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;longitude&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-49b33b7f-170a-4596-a86e-f2e949be2e9f' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-49b33b7f-170a-4596-a86e-f2e949be2e9f' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:                                      (hour: 4, dayofyear: 366,\n",
       "                                                  longitude: 64, latitude: 32,\n",
       "                                                  level: 13)\n",
       "Coordinates:\n",
       "  * dayofyear                                    (dayofyear) int64 1 2 ... 366\n",
       "  * hour                                         (hour) int64 0 6 12 18\n",
       "  * latitude                                     (latitude) float64 -87.19 .....\n",
       "  * level                                        (level) int64 50 100 ... 1000\n",
       "  * longitude                                    (longitude) float64 0.0 ... ...\n",
       "Data variables: (12/28)\n",
       "    10m_u_component_of_wind                      (hour, dayofyear, longitude, latitude) float32 dask.array<chunksize=(4, 366, 64, 32), meta=np.ndarray>\n",
       "    10m_v_component_of_wind                      (hour, dayofyear, longitude, latitude) float32 dask.array<chunksize=(4, 366, 64, 32), meta=np.ndarray>\n",
       "    10m_wind_speed                               (hour, dayofyear, longitude, latitude) float32 dask.array<chunksize=(4, 366, 64, 32), meta=np.ndarray>\n",
       "    2m_temperature                               (hour, dayofyear, longitude, latitude) float32 dask.array<chunksize=(4, 366, 64, 32), meta=np.ndarray>\n",
       "    geopotential                                 (hour, dayofyear, level, longitude, latitude) float32 dask.array<chunksize=(4, 366, 13, 64, 32), meta=np.ndarray>\n",
       "    mean_sea_level_pressure                      (hour, dayofyear, longitude, latitude) float32 dask.array<chunksize=(4, 366, 64, 32), meta=np.ndarray>\n",
       "    ...                                           ...\n",
       "    total_precipitation_6hr_seeps_dry_fraction   (hour, dayofyear, longitude, latitude) float32 dask.array<chunksize=(4, 366, 64, 32), meta=np.ndarray>\n",
       "    total_precipitation_6hr_seeps_threshold      (hour, dayofyear, longitude, latitude) float32 dask.array<chunksize=(4, 366, 64, 32), meta=np.ndarray>\n",
       "    u_component_of_wind                          (hour, dayofyear, level, longitude, latitude) float32 dask.array<chunksize=(4, 366, 13, 64, 32), meta=np.ndarray>\n",
       "    v_component_of_wind                          (hour, dayofyear, level, longitude, latitude) float32 dask.array<chunksize=(4, 366, 13, 64, 32), meta=np.ndarray>\n",
       "    vertical_velocity                            (hour, dayofyear, level, longitude, latitude) float32 dask.array<chunksize=(4, 366, 13, 64, 32), meta=np.ndarray>\n",
       "    wind_speed                                   (hour, dayofyear, level, longitude, latitude) float32 dask.array<chunksize=(4, 366, 13, 64, 32), meta=np.ndarray>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "climatology = xr.open_zarr(climatology_path)\n",
    "climatology"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "034295ed-433f-4731-a932-37b423df1477",
   "metadata": {},
   "source": [
    "### Set up WeatherBench configuration\n",
    "\n",
    "Next, we will define a bunch of configuration instances to specify exactly what we want to evaluate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "097c47bb-38ab-4d7d-94a9-794f9fd3353d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from weatherbench2 import config"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "7ce91925-4c6b-4fd5-8ac0-b828e3eca417",
   "metadata": {},
   "source": [
    "#### Data configuration\n",
    "\n",
    "The file paths are defined in a Paths config object, alongside an output directory:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4d647baf-6711-4e8e-8d36-ec821fed6c32",
   "metadata": {},
   "outputs": [],
   "source": [
    "paths = config.Paths(\n",
    "    forecast=forecast_path,\n",
    "    obs=obs_path,\n",
    "    output_dir='./',   # Directory to save evaluation results\n",
    ")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "18e6ecd0-316e-4c1b-a349-06394ad4d229",
   "metadata": {},
   "source": [
    "In addition, we specify a Selection object that selects the variables and time period to be evaluated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d22b276a-613a-4ac0-93cc-45e2797441e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "selection = config.Selection(\n",
    "    variables=[\n",
    "        'geopotential',\n",
    "        '2m_temperature'\n",
    "    ],\n",
    "    levels=[500, 700, 850],\n",
    "    time_slice=slice('2020-01-01', '2020-12-31'),\n",
    ")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "d5286330-d0f4-4da9-8394-f8536790dd9c",
   "metadata": {},
   "source": [
    "Together they make up the Data config:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e299d579-7ece-46f3-828c-b169a80a564c",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_config = config.Data(selection=selection, paths=paths)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "c3309e72-db02-4eae-b84d-4bfb49b2fa31",
   "metadata": {},
   "source": [
    "#### Evaluation configuration\n",
    "\n",
    "Next, we can defined which evaluation we want to run. To do so, we can define a dictionary of `config.Eval`s, each of which will be evaluated separately and saved to a different file. Eval instances contain the metrics objects, defined in metrics.py.\n",
    "\n",
    "Note that for ACC, we additionally need to pass the climatology opened earlier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e6d14cd6-b379-4838-8ea4-9ce66334bd6e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from weatherbench2.metrics import MSE, ACC\n",
    "\n",
    "eval_configs = {\n",
    "  'deterministic': config.Eval(\n",
    "      metrics={\n",
    "          'mse': MSE(), \n",
    "          'acc': ACC(climatology=climatology) \n",
    "      },\n",
    "  )\n",
    "}"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "da6e221c-7a40-40d5-bdc5-73ab7021b120",
   "metadata": {},
   "source": [
    "The evaluation configs also have an option to evaluate particular regions, such as a geographical lat-lon box. These are defined as region objects defined in regions.py. All regions will be evaluated separately and saved as an additional dimension in the dataset. If no region is specified, evaluation will be done globally."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "1c00935d-ff70-4a80-b96c-269b3a4753a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "from weatherbench2.regions import SliceRegion, ExtraTropicalRegion\n",
    "\n",
    "regions = {\n",
    "    'global': SliceRegion(),\n",
    "    'tropics': SliceRegion(lat_slice=slice(-20, 20)),\n",
    "    'extra-tropics': ExtraTropicalRegion(),\n",
    "}\n",
    "\n",
    "eval_configs = {\n",
    "  'deterministic': config.Eval(\n",
    "      metrics={\n",
    "          'mse': MSE(), \n",
    "          'acc': ACC(climatology=climatology) \n",
    "      },\n",
    "      regions=regions\n",
    "  )\n",
    "}"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "f46a917d-6b99-491b-87da-53a3d567e0c8",
   "metadata": {},
   "source": [
    "### Evaluate\n",
    "\n",
    "Now, we are already done and can run the evaluation. We can do so in memory for smaller datasets or as a Beam pipeline (recommended for anything larger than 64x32 resolution)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "49464fa8-7017-4d82-8f40-00e265289f61",
   "metadata": {},
   "outputs": [],
   "source": [
    "from weatherbench2.evaluation import evaluate_in_memory, evaluate_with_beam"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b0a201dd-c32a-4df1-b8f2-09be12781016",
   "metadata": {},
   "outputs": [],
   "source": [
    "evaluate_in_memory(data_config, eval_configs)   # Takes around 5 minutes"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "dee5ea2e-7909-4730-90d5-fb1a227e14cd",
   "metadata": {},
   "source": [
    "For the Beam version, evaluation is split across chunks. The chunk sizes can be specified alongside the Beam runner. For more detail on running Beam in a distributed fashion on the cloud, see [this guide](https://weatherbench2.readthedocs.io/en/latest/beam-in-the-cloud.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "7947f016-80ac-4e8b-9cb5-0adf9a9c9b5f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# evaluation.evaluate_with_beam(\n",
    "#     data_config,\n",
    "#     eval_configs,\n",
    "#     runner='DirectRunner',\n",
    "#     input_chunks={'time': 20},\n",
    "# )"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "50117845-6c07-4012-9318-08932bc5fc9f",
   "metadata": {},
   "source": [
    "### Results"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "0a15ffd6-8d02-47ef-8812-a18693827cc8",
   "metadata": {},
   "source": [
    "Results are saved as NetCDF files under the name of the eval config."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "a4f9d35b-7907-423f-b0fa-15a89e03dcd1",
   "metadata": {},
   "outputs": [
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:         (lead_time: 41, region: 3, level: 3, metric: 2)\n",
       "Coordinates:\n",
       "  * lead_time       (lead_time) timedelta64[ns] 0 days 00:00:00 ... 10 days 0...\n",
       "  * region          (region) object &#x27;global&#x27; &#x27;tropics&#x27; &#x27;extra-tropics&#x27;\n",
       "  * level           (level) int32 500 700 850\n",
       "  * metric          (metric) object &#x27;acc&#x27; &#x27;mse&#x27;\n",
       "Data variables:\n",
       "    geopotential    (metric, region, lead_time, level) float64 ...\n",
       "    2m_temperature  (metric, region, lead_time) float64 ...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-df3ecb48-3d43-40a2-8ff5-05adb1421f1d' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-df3ecb48-3d43-40a2-8ff5-05adb1421f1d' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>lead_time</span>: 41</li><li><span class='xr-has-index'>region</span>: 3</li><li><span class='xr-has-index'>level</span>: 3</li><li><span class='xr-has-index'>metric</span>: 2</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-76d151b4-b36f-46f4-ad03-14c44a1569ef' class='xr-section-summary-in' type='checkbox'  checked><label for='section-76d151b4-b36f-46f4-ad03-14c44a1569ef' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lead_time</span></div><div class='xr-var-dims'>(lead_time)</div><div class='xr-var-dtype'>timedelta64[ns]</div><div class='xr-var-preview xr-preview'>0 days 00:00:00 ... 10 days 00:0...</div><input id='attrs-1c4ebb06-b2a0-42c8-97a8-501ec63e88f9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1c4ebb06-b2a0-42c8-97a8-501ec63e88f9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7f32fb8f-a4b8-49e6-b0b6-125999bd7cf3' class='xr-var-data-in' type='checkbox'><label for='data-7f32fb8f-a4b8-49e6-b0b6-125999bd7cf3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([              0,  21600000000000,  43200000000000,  64800000000000,\n",
       "        86400000000000, 108000000000000, 129600000000000, 151200000000000,\n",
       "       172800000000000, 194400000000000, 216000000000000, 237600000000000,\n",
       "       259200000000000, 280800000000000, 302400000000000, 324000000000000,\n",
       "       345600000000000, 367200000000000, 388800000000000, 410400000000000,\n",
       "       432000000000000, 453600000000000, 475200000000000, 496800000000000,\n",
       "       518400000000000, 540000000000000, 561600000000000, 583200000000000,\n",
       "       604800000000000, 626400000000000, 648000000000000, 669600000000000,\n",
       "       691200000000000, 712800000000000, 734400000000000, 756000000000000,\n",
       "       777600000000000, 799200000000000, 820800000000000, 842400000000000,\n",
       "       864000000000000], dtype=&#x27;timedelta64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>region</span></div><div class='xr-var-dims'>(region)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;global&#x27; &#x27;tropics&#x27; &#x27;extra-tropics&#x27;</div><input id='attrs-9fcc3ad3-26de-4897-b9d8-82b14b9124f8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9fcc3ad3-26de-4897-b9d8-82b14b9124f8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6def1fa8-149c-4533-ab2b-a65073e23638' class='xr-var-data-in' type='checkbox'><label for='data-6def1fa8-149c-4533-ab2b-a65073e23638' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;global&#x27;, &#x27;tropics&#x27;, &#x27;extra-tropics&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>level</span></div><div class='xr-var-dims'>(level)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>500 700 850</div><input id='attrs-f9d53d39-7490-466a-b227-f7b4b747d7b6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f9d53d39-7490-466a-b227-f7b4b747d7b6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dbe327bf-f4b9-4eac-8c3b-e501a45652ac' class='xr-var-data-in' type='checkbox'><label for='data-dbe327bf-f4b9-4eac-8c3b-e501a45652ac' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([500, 700, 850], dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>metric</span></div><div class='xr-var-dims'>(metric)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;acc&#x27; &#x27;mse&#x27;</div><input id='attrs-6ef0510e-eb77-4250-b5d2-106453156f51' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6ef0510e-eb77-4250-b5d2-106453156f51' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-03188856-c2df-4ed6-8f25-8c1118e10d9f' class='xr-var-data-in' type='checkbox'><label for='data-03188856-c2df-4ed6-8f25-8c1118e10d9f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;acc&#x27;, &#x27;mse&#x27;], dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-06181ff0-a9c8-46f9-b542-400e328cf927' class='xr-section-summary-in' type='checkbox'  checked><label for='section-06181ff0-a9c8-46f9-b542-400e328cf927' class='xr-section-summary' >Data variables: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>geopotential</span></div><div class='xr-var-dims'>(metric, region, lead_time, level)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-1930e34f-30f9-409e-a5ab-23debbfefc1f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1930e34f-30f9-409e-a5ab-23debbfefc1f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-042e3d30-1d76-4bc8-bab3-3cc1f0d7697d' class='xr-var-data-in' type='checkbox'><label for='data-042e3d30-1d76-4bc8-bab3-3cc1f0d7697d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[738 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>2m_temperature</span></div><div class='xr-var-dims'>(metric, region, lead_time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-168e8b2b-bcce-4701-9783-0179a16fa55d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-168e8b2b-bcce-4701-9783-0179a16fa55d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-44e5896d-7be8-4ce2-97ec-cb86802df2ed' class='xr-var-data-in' type='checkbox'><label for='data-44e5896d-7be8-4ce2-97ec-cb86802df2ed' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[246 values with dtype=float64]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-7fd0400b-d28b-4f29-9ead-2e9e81ea2352' class='xr-section-summary-in' type='checkbox'  ><label for='section-7fd0400b-d28b-4f29-9ead-2e9e81ea2352' class='xr-section-summary' >Indexes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>lead_time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-a0004c47-c0bc-4f75-984d-9333b532f09d' class='xr-index-data-in' type='checkbox'/><label for='index-a0004c47-c0bc-4f75-984d-9333b532f09d' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(TimedeltaIndex([ &#x27;0 days 00:00:00&#x27;,  &#x27;0 days 06:00:00&#x27;,  &#x27;0 days 12:00:00&#x27;,\n",
       "                 &#x27;0 days 18:00:00&#x27;,  &#x27;1 days 00:00:00&#x27;,  &#x27;1 days 06:00:00&#x27;,\n",
       "                 &#x27;1 days 12:00:00&#x27;,  &#x27;1 days 18:00:00&#x27;,  &#x27;2 days 00:00:00&#x27;,\n",
       "                 &#x27;2 days 06:00:00&#x27;,  &#x27;2 days 12:00:00&#x27;,  &#x27;2 days 18:00:00&#x27;,\n",
       "                 &#x27;3 days 00:00:00&#x27;,  &#x27;3 days 06:00:00&#x27;,  &#x27;3 days 12:00:00&#x27;,\n",
       "                 &#x27;3 days 18:00:00&#x27;,  &#x27;4 days 00:00:00&#x27;,  &#x27;4 days 06:00:00&#x27;,\n",
       "                 &#x27;4 days 12:00:00&#x27;,  &#x27;4 days 18:00:00&#x27;,  &#x27;5 days 00:00:00&#x27;,\n",
       "                 &#x27;5 days 06:00:00&#x27;,  &#x27;5 days 12:00:00&#x27;,  &#x27;5 days 18:00:00&#x27;,\n",
       "                 &#x27;6 days 00:00:00&#x27;,  &#x27;6 days 06:00:00&#x27;,  &#x27;6 days 12:00:00&#x27;,\n",
       "                 &#x27;6 days 18:00:00&#x27;,  &#x27;7 days 00:00:00&#x27;,  &#x27;7 days 06:00:00&#x27;,\n",
       "                 &#x27;7 days 12:00:00&#x27;,  &#x27;7 days 18:00:00&#x27;,  &#x27;8 days 00:00:00&#x27;,\n",
       "                 &#x27;8 days 06:00:00&#x27;,  &#x27;8 days 12:00:00&#x27;,  &#x27;8 days 18:00:00&#x27;,\n",
       "                 &#x27;9 days 00:00:00&#x27;,  &#x27;9 days 06:00:00&#x27;,  &#x27;9 days 12:00:00&#x27;,\n",
       "                 &#x27;9 days 18:00:00&#x27;, &#x27;10 days 00:00:00&#x27;],\n",
       "               dtype=&#x27;timedelta64[ns]&#x27;, name=&#x27;lead_time&#x27;, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>region</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-53ccfab6-daa7-4c5a-8311-9306ef956124' class='xr-index-data-in' type='checkbox'/><label for='index-53ccfab6-daa7-4c5a-8311-9306ef956124' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;global&#x27;, &#x27;tropics&#x27;, &#x27;extra-tropics&#x27;], dtype=&#x27;object&#x27;, name=&#x27;region&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>level</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-02afe4fc-0357-48aa-b8f3-bdc4a60ed97c' class='xr-index-data-in' type='checkbox'/><label for='index-02afe4fc-0357-48aa-b8f3-bdc4a60ed97c' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([500, 700, 850], dtype=&#x27;int32&#x27;, name=&#x27;level&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>metric</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-60d3e7a7-796d-49f7-aed3-39e27d43a350' class='xr-index-data-in' type='checkbox'/><label for='index-60d3e7a7-796d-49f7-aed3-39e27d43a350' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;acc&#x27;, &#x27;mse&#x27;], dtype=&#x27;object&#x27;, name=&#x27;metric&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d188a294-e248-48fe-8c5f-cc3a7ff3d269' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-d188a294-e248-48fe-8c5f-cc3a7ff3d269' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:         (lead_time: 41, region: 3, level: 3, metric: 2)\n",
       "Coordinates:\n",
       "  * lead_time       (lead_time) timedelta64[ns] 0 days 00:00:00 ... 10 days 0...\n",
       "  * region          (region) object 'global' 'tropics' 'extra-tropics'\n",
       "  * level           (level) int32 500 700 850\n",
       "  * metric          (metric) object 'acc' 'mse'\n",
       "Data variables:\n",
       "    geopotential    (metric, region, lead_time, level) float64 ...\n",
       "    2m_temperature  (metric, region, lead_time) float64 ..."
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results = xr.open_dataset('./deterministic.nc')\n",
    "results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "840db828-d6f0-4d78-b900-eac135c754a5",
   "metadata": {},
   "source": [
    "Note that to compute the RMSE, we follow ECMWF's convention by taking the square root after the time mean. To do this in WB2, first compute the MSE and then take the square root of the saved MSE results. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "ea579fba-ca76-4cd4-8294-27e8db9eaa60",
   "metadata": {},
   "outputs": [],
   "source": [
    "results = xr.concat(\n",
    "    [\n",
    "    results,\n",
    "    results.sel(metric=['mse']).assign_coords(metric=['rmse']) ** 0.5\n",
    "    ],\n",
    "    dim='metric'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "cf8f10ae-c230-46e1-83dd-549dfb4248d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "results['geopotential'].sel(metric='rmse', level=500, region='global').plot();"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "4ddb45e4-9ac1-427c-8290-35f2d5e6a7dc",
   "metadata": {},
   "source": [
    "### Next steps\n",
    "\n",
    "This quickstart guide shows the basic functionality of the WeatherBench evaluation code but there is more to explore. \n",
    "\n",
    "For running evaluation from the command line, see [this guide](https://weatherbench2.readthedocs.io/en/latest/command-line-scripts.html). For a complete overview of the entire evaluation workflow, check out the [\"submission\" guide](https://weatherbench2.readthedocs.io/en/latest/submit.html)."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:weatherbench2]",
   "language": "python",
   "name": "conda-env-weatherbench2-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
